Song James, Verbeeck Johan, Huang Bo, Hoaglin David C, Gamalo-Siebers Margaret, Seifu Yodit, Wang Duolao, Cooner Freda, Dong Gaohong
BeiGene, Ridgefield Park, New Jersey, USA.
DSI, I-Biostat, University Hasselt, Hasselt, Belgium.
J Biopharm Stat. 2023 Mar;33(2):140-150. doi: 10.1080/10543406.2022.2089156. Epub 2022 Aug 10.
Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. An important limitation of these statistics is their inability to adjust for covariates other than by stratified analysis. Because the win ratio does not account for ties, the win odds, a modification that includes ties, has attracted attention. We review and combine information on the win odds to articulate the statistical inferences for the win odds. We also show alternative variance estimators based on the exact permutation and bootstrap as well as statistical inference via the probabilistic index. Finally, we extend multiple-covariate regression probabilistic index models to the win odds with a univariate outcome. As an illustration we apply the regression models to the data in the CHARM trial.
广义成对比较和获胜统计量（即胜率、获胜概率和净效益）在分析和解释临床试验中的多个复合结局时具有优势。这些统计量的一个重要局限性是，除了分层分析外，它们无法对协变量进行调整。由于胜率不考虑平局情况，因此包括平局情况的改进版获胜概率受到了关注。我们回顾并整合了关于获胜概率的信息，以阐述获胜概率的统计推断。我们还展示了基于精确排列和自助法的替代方差估计量，以及通过概率指数进行的统计推断。最后，我们将多协变量回归概率指数模型扩展到具有单变量结局的获胜概率。作为示例，我们将回归模型应用于CHARM试验的数据。
